1. Technical Field
The invention generally relates to predictive model development. More particularly, the invention relates to a method and system for modeling impact of future incremental balance that are unknown at scoring date in credit quality assessments at the scoring date.
2. Background Information
In the United States, at the end of 2006, total household debt stood at approximately $12.8 trillion, including $9.7 trillion mortgage debt and $2.4 trillion credit debt. Consumer advocacy groups are concerned that credit card indebtedness and rising interest rates associated with non-traditional mortgages will continue to create strain on lower-income groups. Adding to the controversy are such factors as increasing credit card monthly minimum payment requirements which reduce consumers' cash on hand due to higher contractual debt repayment requirements; a negative savings rate, and increased ARM mortgage debt obligations when interest rates increase and/or adjustable rates reset. In combination, these factors may indicate that consumers are incurring more debt than they can comfortably handle and that the costs to maintain these debts is increasing.
Such factors lead to pressure on regulators and legislators to enact laws and regulations that govern the manner in which lenders are allowed to target consumers for additional offers of debt. As lenders continue to seek growth and higher profitability, legislation is under consideration in many locales to reduce consumer over-indebtedness. For example, in the United States, the FDIC (Federal Deposit Insurance Corporation) has expressed concern over rising interest rates and non-traditional instruments. The UK, Australia and South Africa have all enacted national laws that mandate fair and responsible lending practices.
Consumers, nevertheless, still want flexible lifestyle choices and demand the financing options to support their lifestyles. At the same time, however, even in view of the new bankruptcy law in the United States, consumers are expected to resume filing for bankruptcy in record numbers and foreclosure rates are at all time highs.
Lenders have a number of tools for evaluating an individual's debt capacity. Lenders, for example, use debt-to-income ratios in determining loan amounts or credit lines. They may calculate the current debt service plus the monthly payment for the new loan and divide the resulting value by the total monthly income. If the ratio is under a predetermined threshold, 35% for example, the lender approves the loan. Otherwise, the lender may reject the loan application or counter with a loan amount that fits the predetermined ceiling.
A drawback of the debt-to-income ratio is that it can be naïve: self-reported income is subject to exaggeration, income estimators are typically unreliable, credit-savvy consumers often have means to temporarily supplement income and different consumers have different living expenses and therefore might be able to afford more or less than 35% depending on lifestyle, family size, region of residence, and unreported sources of income/expense, e.g. alimony.
Standard modeling problems typically involve two snapshots. One is called the predictive (pred snapshot, scoring date) and is a measure of consumer credit at time 1 (T1), the other is the performance (perf) snapshot and is a measure of consumer behavior at time two (T2), a fixed time period following T1. Typically, at T1, a model is built to predict performance at time T2. This is true of any standard analytic model where at T2, there would be one of a variety of performances—risk, revenue, fraud, attrition, etc. This modeling approach is common not just in financial services and credit, but in insurance and other areas. Performance is generally dichotomous—representing in the credit risk scoring field, for example—“goods” and “bads.” A “bad” may be a default, or a delinquency, for example; a “good” is payment as agreed. Using credit bureau data and risk scores, it is possible to paint a picture of one's credit at one time as it relates to what the picture will look like at a later time.
These models, however, only include the consumer's history at the predictive date and prior, and as such, cannot address the relative impact of debts incurred shortly after the scoring date. This is an important distinction because most uses of a predictive score are designed to make a credit decision where a change after the scoring date is particularly likely.
Typically, a lender acquires a score at a given time (aka, the scoring date) to make a lending decision or offer to a consumer at that time. In securing a score the lender desires to predict the borrower's likely performance. In attempting to predict borrower performance, the lender may rank order borrowers based on a score such as a FICO (FAIR ISMC CORPORATION, Minneapolis Minn.) score, a behavior score, some other kind of risk or revenue score in order to predict borrower risk performance or profitability. However, such “fixed snapshot” scores do not reflect likely consequences of the lenders ensuing offer, such as incurring additional debt after the scoring date.
Moreover, experience has shown that credit bureau data is considerably more reliable than consumer-supplied data for determining creditworthiness such as used in many income based measures. Lenders, therefore often use risk scores calculated from credit bureau data in evaluating creditworthiness, for example the FICO score. The FICO score predicts default risk from a credit bureau report snapshot. While the FICO score accurately assesses default risk based on static credit bureau information, it, like other “snapshot based” scores does not consider information not yet represented on credit reports, such as new debt. Additionally, while people with the same score represent the same absolute default risk, consumers within a specific risk cohort likely have different debt profiles and would consequently respond differently to the range of subsequent actions taken by themselves or their lenders after a snapshot in time.
The makeup of such different profiles represents potentially different capacities to handle future incremental debt without default, even among individuals having the same risk score. It would therefore be a great advantage for a lender to be able to obtain an improved understanding of a consumer's ability to responsibly manage future debt within risk levels when offering new or extended credit.
There exists therefore a great need in the art for a reliable way to address the industry problem of evaluating consumer capacity to handle incremental debt.